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  <div class="section" id="module-pyvib.features">
<span id="signal-features"></span><h1>Signal features<a class="headerlink" href="#module-pyvib.features" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="pyvib.features.LOG">
<code class="descclassname">pyvib.features.</code><code class="descname">LOG</code><span class="sig-paren">(</span><em>x</em><span class="sig-paren">)</span><a class="headerlink" href="#pyvib.features.LOG" title="Permalink to this definition">¶</a></dt>
<dd><p>Exponential of the mean absolute logarithm</p>
<table class="docutils field-list" frame="void" rules="none">
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>x</strong> (<em>float 1D array</em>) – Signal</td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="function">
<dt id="pyvib.features.SDofIHC">
<code class="descclassname">pyvib.features.</code><code class="descname">SDofIHC</code><span class="sig-paren">(</span><em>x</em><span class="sig-paren">)</span><a class="headerlink" href="#pyvib.features.SDofIHC" title="Permalink to this definition">¶</a></dt>
<dd><p>Standard deviation of inverse hyperbolic cosine</p>
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>x</strong> (<em>float 1D array</em>) – Signal</li>
<li><strong>from paper</strong> (<em>Taken</em>) – </li>
<li><strong>Model-Based Method for Remaining Useful Life Prediction of Machinery</strong> (<em>A</em>) – </li>
<li><strong>Lei et al.</strong> (<em>Yaguo</em>) – </li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="function">
<dt id="pyvib.features.SDofIHS">
<code class="descclassname">pyvib.features.</code><code class="descname">SDofIHS</code><span class="sig-paren">(</span><em>x</em><span class="sig-paren">)</span><a class="headerlink" href="#pyvib.features.SDofIHS" title="Permalink to this definition">¶</a></dt>
<dd><p>Standard deviation of inverse hyperbolic sine</p>
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>x</strong> (<em>float 1D array</em>) – Signal</li>
<li><strong>from paper</strong> (<em>Taken</em>) – </li>
<li><strong>Model-Based Method for Remaining Useful Life Prediction of Machinery</strong> (<em>A</em>) – </li>
<li><strong>Lei et al.</strong> (<em>Yaguo</em>) – </li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="function">
<dt id="pyvib.features.absoluteMean">
<code class="descclassname">pyvib.features.</code><code class="descname">absoluteMean</code><span class="sig-paren">(</span><em>x</em><span class="sig-paren">)</span><a class="headerlink" href="#pyvib.features.absoluteMean" title="Permalink to this definition">¶</a></dt>
<dd><p>Get absolute mean of signal</p>
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>x</strong> (<em>float 1D array</em>) – Signal</td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="function">
<dt id="pyvib.features.approximateEntropy">
<code class="descclassname">pyvib.features.</code><code class="descname">approximateEntropy</code><span class="sig-paren">(</span><em>U</em>, <em>N=1000</em>, <em>m=2</em>, <em>r=None</em><span class="sig-paren">)</span><a class="headerlink" href="#pyvib.features.approximateEntropy" title="Permalink to this definition">¶</a></dt>
<dd><p>Approximate the entropy of a signal</p>
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>U</strong> (<em>float 1D array</em>) – Signal</li>
<li><strong>N</strong> (<em>int</em><em>, </em><em>optional</em>) – </li>
<li><strong>m</strong> (<em>int</em><em>, </em><em>optional</em>) – </li>
<li><strong>r</strong> (<em>float</em><em>, </em><em>optional</em>) – </li>
<li><strong>https</strong> (<em>//en.wikipedia.org/wiki/Approximate_entropy</em>) – </li>
<li><strong>values taken from</strong> (<em>Default</em>) – </li>
<li><strong>Ruqiang</strong><strong>, </strong><strong>and Robert X. Gao.</strong> (<em>Yan</em><em>,</em>) – </li>
<li><strong>entropy as a diagnostic tool for machine health monitoring.&quot;</strong> (<em>&quot;Approximate</em>) – </li>
<li><strong>Systems and Signal Processing 21.2</strong><strong> (</strong><strong>2007</strong><strong>)</strong> (<em>Mechanical</em>) – </li>
</ul>
</td>
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</table>
</dd></dl>

<dl class="function">
<dt id="pyvib.features.bearingEnergy">
<code class="descclassname">pyvib.features.</code><code class="descname">bearingEnergy</code><span class="sig-paren">(</span><em>Y</em>, <em>df</em>, <em>X</em>, <em>bearing</em><span class="sig-paren">)</span><a class="headerlink" href="#pyvib.features.bearingEnergy" title="Permalink to this definition">¶</a></dt>
<dd><p>Energy within band of typical characteristic frequencies</p>
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>Y</strong> (<em>float 1D array</em>) – Spectrum aplitude</li>
<li><strong>df</strong> (<em>float</em>) – Frequncy spacing in Hz</li>
<li><strong>X</strong> (<em>float</em>) – Shaft speed in Hz</li>
<li><strong>bearing</strong> (<em>float 1D array</em>) – Bearing characteristic frequencies in orders (i.e. per revolution)
bearing[0] - Inner race
bearing[1] - 2x roller spin frequency
bearing[2] - Cage frequency
bearing[3] - Outer race frequency</li>
</ul>
</td>
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</dd></dl>

<dl class="function">
<dt id="pyvib.features.clearanceFactor">
<code class="descclassname">pyvib.features.</code><code class="descname">clearanceFactor</code><span class="sig-paren">(</span><em>x</em><span class="sig-paren">)</span><a class="headerlink" href="#pyvib.features.clearanceFactor" title="Permalink to this definition">¶</a></dt>
<dd><p>Clearance factor</p>
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>x</strong> (<em>float 1D array</em>) – Signal</td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="function">
<dt id="pyvib.features.crestFactor">
<code class="descclassname">pyvib.features.</code><code class="descname">crestFactor</code><span class="sig-paren">(</span><em>x</em><span class="sig-paren">)</span><a class="headerlink" href="#pyvib.features.crestFactor" title="Permalink to this definition">¶</a></dt>
<dd><p>Crest factor</p>
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</tr>
</tbody>
</table>
</dd></dl>

<dl class="function">
<dt id="pyvib.features.frequencyCenter">
<code class="descclassname">pyvib.features.</code><code class="descname">frequencyCenter</code><span class="sig-paren">(</span><em>f</em>, <em>Y</em><span class="sig-paren">)</span><a class="headerlink" href="#pyvib.features.frequencyCenter" title="Permalink to this definition">¶</a></dt>
<dd><p>Frequency center of spectrum</p>
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>f</strong> (<em>float 1D array</em>) – Frequency of FFT</li>
<li><strong>Y</strong> (<em>float 1D array</em>) – Amplitude of FFT</li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="function">
<dt id="pyvib.features.impulseFactor">
<code class="descclassname">pyvib.features.</code><code class="descname">impulseFactor</code><span class="sig-paren">(</span><em>x</em><span class="sig-paren">)</span><a class="headerlink" href="#pyvib.features.impulseFactor" title="Permalink to this definition">¶</a></dt>
<dd><p>Impulse factor</p>
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>x</strong> (<em>float 1D array</em>) – Signal</td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="function">
<dt id="pyvib.features.kurtosis">
<code class="descclassname">pyvib.features.</code><code class="descname">kurtosis</code><span class="sig-paren">(</span><em>x</em><span class="sig-paren">)</span><a class="headerlink" href="#pyvib.features.kurtosis" title="Permalink to this definition">¶</a></dt>
<dd><p>Get kurtosis value</p>
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>x</strong> (<em>float 1D array</em>) – Signal</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><strong>K</strong> – Kurtosis</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body">float</td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="function">
<dt id="pyvib.features.kurtosisFactor">
<code class="descclassname">pyvib.features.</code><code class="descname">kurtosisFactor</code><span class="sig-paren">(</span><em>x</em><span class="sig-paren">)</span><a class="headerlink" href="#pyvib.features.kurtosisFactor" title="Permalink to this definition">¶</a></dt>
<dd><p>Kurtosis factor</p>
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>x</strong> (<em>float 1D array</em>) – Signal</td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="function">
<dt id="pyvib.features.maxToMinPowerDensityDrop">
<code class="descclassname">pyvib.features.</code><code class="descname">maxToMinPowerDensityDrop</code><span class="sig-paren">(</span><em>Y</em>, <em>df</em>, <em>X</em>, <em>bearing</em><span class="sig-paren">)</span><a class="headerlink" href="#pyvib.features.maxToMinPowerDensityDrop" title="Permalink to this definition">¶</a></dt>
<dd><p>Maximum to minimum power density drop</p>
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>Y</strong> (<em>float 1D array</em>) – Spectrum aplitude</li>
<li><strong>df</strong> (<em>float</em>) – Frequncy spacing in Hz</li>
<li><strong>X</strong> (<em>float</em>) – Shaft speed in Hz</li>
<li><strong>bearing</strong> (<em>float 1D array</em>) – Bearing characteristic frequencies in orders (i.e. per revolution)
bearing[0] - Inner race
bearing[1] - 2x roller spin frequency
bearing[2] - Cage frequency
bearing[3] - Outer race frequency</li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="function">
<dt id="pyvib.features.medianFrequency">
<code class="descclassname">pyvib.features.</code><code class="descname">medianFrequency</code><span class="sig-paren">(</span><em>Y</em>, <em>df</em><span class="sig-paren">)</span><a class="headerlink" href="#pyvib.features.medianFrequency" title="Permalink to this definition">¶</a></dt>
<dd><p>Median frequency of a spectrum</p>
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>Y</strong> (<em>float 1D array</em>) – Spectrum aplitude</li>
<li><strong>df</strong> (<em>float</em>) – Frequency spacing between bins</li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="function">
<dt id="pyvib.features.myoPulsePercentage">
<code class="descclassname">pyvib.features.</code><code class="descname">myoPulsePercentage</code><span class="sig-paren">(</span><em>x</em>, <em>eps=5.0</em><span class="sig-paren">)</span><a class="headerlink" href="#pyvib.features.myoPulsePercentage" title="Permalink to this definition">¶</a></dt>
<dd><p>Myo pulse percentage
Sum of all impulses greater than a threshold eps</p>
<table class="docutils field-list" frame="void" rules="none">
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<li><strong>x</strong> (<em>float 1D array</em>) – Signal</li>
<li><strong>eps</strong> (<em>float</em><em>, </em><em>opional</em>) – Threshold</li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="function">
<dt id="pyvib.features.peakToPeak">
<code class="descclassname">pyvib.features.</code><code class="descname">peakToPeak</code><span class="sig-paren">(</span><em>x</em><span class="sig-paren">)</span><a class="headerlink" href="#pyvib.features.peakToPeak" title="Permalink to this definition">¶</a></dt>
<dd><p>Peak-to-peak of signal</p>
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>x</strong> (<em>float 1D array</em>) – Signal</td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="function">
<dt id="pyvib.features.rms">
<code class="descclassname">pyvib.features.</code><code class="descname">rms</code><span class="sig-paren">(</span><em>y</em><span class="sig-paren">)</span><a class="headerlink" href="#pyvib.features.rms" title="Permalink to this definition">¶</a></dt>
<dd><p>Get RMS vlaue</p>
<table class="docutils field-list" frame="void" rules="none">
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>y</strong> (<em>float 1D array</em>) – Signal</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><strong>RMS</strong> – RMS</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body">float</td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="function">
<dt id="pyvib.features.rootMeanSquareFrequency">
<code class="descclassname">pyvib.features.</code><code class="descname">rootMeanSquareFrequency</code><span class="sig-paren">(</span><em>f</em>, <em>Y</em><span class="sig-paren">)</span><a class="headerlink" href="#pyvib.features.rootMeanSquareFrequency" title="Permalink to this definition">¶</a></dt>
<dd><p>Root mean square frequency</p>
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>f</strong> (<em>float 1D array</em>) – Frequency of FFT</li>
<li><strong>Y</strong> (<em>float 1D array</em>) – Amplitude of FFT</li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="function">
<dt id="pyvib.features.shapeFactor">
<code class="descclassname">pyvib.features.</code><code class="descname">shapeFactor</code><span class="sig-paren">(</span><em>x</em><span class="sig-paren">)</span><a class="headerlink" href="#pyvib.features.shapeFactor" title="Permalink to this definition">¶</a></dt>
<dd><p>Shape factor</p>
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</tr>
</tbody>
</table>
</dd></dl>

<dl class="function">
<dt id="pyvib.features.skewnessFactor">
<code class="descclassname">pyvib.features.</code><code class="descname">skewnessFactor</code><span class="sig-paren">(</span><em>x</em><span class="sig-paren">)</span><a class="headerlink" href="#pyvib.features.skewnessFactor" title="Permalink to this definition">¶</a></dt>
<dd><p>Skewness factor</p>
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</tr>
</tbody>
</table>
</dd></dl>

<dl class="function">
<dt id="pyvib.features.slopeSignChange">
<code class="descclassname">pyvib.features.</code><code class="descname">slopeSignChange</code><span class="sig-paren">(</span><em>x</em>, <em>epsilon=0.5</em><span class="sig-paren">)</span><a class="headerlink" href="#pyvib.features.slopeSignChange" title="Permalink to this definition">¶</a></dt>
<dd><p>Slope sign change</p>
<table class="docutils field-list" frame="void" rules="none">
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>x</strong> (<em>float 1D array</em>) – Signal</li>
<li><strong>epsilon</strong> (<em>float</em><em>, </em><em>optional</em>) – </li>
<li><strong>from paper</strong> (<em>Taken</em>) – Nayana, B. R., and P. Geethanjali.
“Analysis of Statistical Time-Domain Features Effectiveness
in Identification of Bearing Faults From Vibration Signal.”
IEEE Sensors Journal 17.17 (2017): 5618-5625.</li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="function">
<dt id="pyvib.features.snr">
<code class="descclassname">pyvib.features.</code><code class="descname">snr</code><span class="sig-paren">(</span><em>r</em>, <em>Fs</em>, <em>ma=0.05</em>, <em>mb=0.5</em>, <em>cb=3</em>, <em>mc=0.05</em>, <em>md=0.6</em>, <em>c=2</em>, <em>c_ech=0.05</em>, <em>J_min=3</em>, <em>toler=0.1</em><span class="sig-paren">)</span><a class="headerlink" href="#pyvib.features.snr" title="Permalink to this definition">¶</a></dt>
<dd><p>Estimates the Signal-to-noise ratio.</p>
<p>Based on “About periodicity and signal to noise ratio - The
strength of the autocorrelation function.”
by Nadine Martin and Corinne Mailhes</p>
<table class="docutils field-list" frame="void" rules="none">
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<li><strong>r</strong> (<em>float 1D array</em>) – The signal to estimate SNR of</li>
<li><strong>Fs</strong> (<em>float</em>) – Sampling rate</li>
<li><strong>ma</strong> (<em>float</em>) – Lag support start, percentage of r.size</li>
<li><strong>mb</strong> (<em>float</em>) – Lag support end, percentage of r.size</li>
<li><strong>cb</strong> (<em>int</em>) – Tolerance factor</li>
<li><strong>mc</strong> (<em>float</em>) – Lag support start, percentage of r.size</li>
<li><strong>md</strong> (<em>float</em>) – Lag support end, percentage of r.size</li>
<li><strong>c</strong> (<em>int</em>) – Tolerance factor</li>
<li><strong>c_ech</strong> (<em>float</em>) – Tolerance factor</li>
<li><strong>J_min</strong> (<em>int</em>) – Minimum number of detected maxima</li>
<li><strong>toler</strong> (<em>float</em>) – Tolerance factor applied to median</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last"><ul>
<li><p class="first"><strong>SNR_hat</strong> (<em>float</em>) – Estimated SNR ratio in dB</p>
</li>
<li><p class="first"><strong>flags</strong> (<em>list, size=4</em>) – Extra information about the calculation</p>
<p>Element[i]:
0 : boolean</p>
<blockquote>
<div><p>0 if r is not aperiodic (Positive)
1 if r is random noise</p>
</div></blockquote>
<dl class="docutils">
<dt>1 <span class="classifier-delimiter">:</span> <span class="classifier">boolean</span></dt>
<dd><p class="first last">0 if card(ksi) &gt; J_min (Positive)
1 else</p>
</dd>
<dt>2 <span class="classifier-delimiter">:</span> <span class="classifier">float</span></dt>
<dd><p class="first last">Confidence value ratio</p>
</dd>
<dt>3 <span class="classifier-delimiter">:</span> <span class="classifier">float</span></dt>
<dd><p class="first last">Estimated fundamental frequency</p>
</dd>
</dl>
</li>
</ul>
</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="function">
<dt id="pyvib.features.squareMeanRoot">
<code class="descclassname">pyvib.features.</code><code class="descname">squareMeanRoot</code><span class="sig-paren">(</span><em>x</em><span class="sig-paren">)</span><a class="headerlink" href="#pyvib.features.squareMeanRoot" title="Permalink to this definition">¶</a></dt>
<dd><p>Square mean root of signal</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>x</strong> (<em>float 1D array</em>) – Signal</td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="function">
<dt id="pyvib.features.standardmoment">
<code class="descclassname">pyvib.features.</code><code class="descname">standardmoment</code><span class="sig-paren">(</span><em>x</em>, <em>k</em><span class="sig-paren">)</span><a class="headerlink" href="#pyvib.features.standardmoment" title="Permalink to this definition">¶</a></dt>
<dd><p>Get standard moment of choice</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>x</strong> (<em>float 1D array</em>) – Signal</li>
<li><strong>k</strong> (<em>int</em>) – Desired moment</li>
<li><strong>Returns</strong> – </li>
<li><strong>SM</strong> (<em>float</em>) – Standard moment of choice</li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="function">
<dt id="pyvib.features.waveformLength">
<code class="descclassname">pyvib.features.</code><code class="descname">waveformLength</code><span class="sig-paren">(</span><em>x</em><span class="sig-paren">)</span><a class="headerlink" href="#pyvib.features.waveformLength" title="Permalink to this definition">¶</a></dt>
<dd><p>Waveform length of signal</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>x</strong> (<em>float 1D array</em>) – Signal</li>
<li><strong>from paper</strong> (<em>Taken</em>) – Nayana, B. R., and P. Geethanjali.
“Analysis of Statistical Time-Domain Features Effectiveness
in Identification of Bearing Faults From Vibration Signal.”
IEEE Sensors Journal 17.17 (2017): 5618-5625.</li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="function">
<dt id="pyvib.features.willsonAmplitude">
<code class="descclassname">pyvib.features.</code><code class="descname">willsonAmplitude</code><span class="sig-paren">(</span><em>x</em>, <em>epsilon=0.5</em><span class="sig-paren">)</span><a class="headerlink" href="#pyvib.features.willsonAmplitude" title="Permalink to this definition">¶</a></dt>
<dd><p>Willson amplitude</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>x</strong> (<em>float 1D array</em>) – Signal</li>
<li><strong>from paper</strong> (<em>Taken</em>) – Nayana, B. R., and P. Geethanjali.
“Analysis of Statistical Time-Domain Features Effectiveness
in Identification of Bearing Faults From Vibration Signal.”
IEEE Sensors Journal 17.17 (2017): 5618-5625.</li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="function">
<dt id="pyvib.features.zeroCrossing">
<code class="descclassname">pyvib.features.</code><code class="descname">zeroCrossing</code><span class="sig-paren">(</span><em>x</em>, <em>epsilon=0.5</em><span class="sig-paren">)</span><a class="headerlink" href="#pyvib.features.zeroCrossing" title="Permalink to this definition">¶</a></dt>
<dd><p>Zero crossing og signal</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>x</strong> (<em>float 1D array</em>) – Signal</li>
<li><strong>epsilon</strong> (<em>float</em><em>, </em><em>optional</em>) – </li>
<li><strong>from paper</strong> (<em>Taken</em>) – Nayana, B. R., and P. Geethanjali.
“Analysis of Statistical Time-Domain Features Effectiveness
in Identification of Bearing Faults From Vibration Signal.”
IEEE Sensors Journal 17.17 (2017): 5618-5625.</li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>

</div>


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